We take visual odometry as an example. Figure REF  reports the translational drifts of deep visual odometry models over time on the 10th trajectory of the KITTI dataset. Clearly, hybrid VO shows the best performance over supervised VO and unsupervised VO, as the hybrid model benefits from both the mature geometry models of traditional VO algorithms and the strong capacity for feature extraction of deep learning. Although supervised VO still outperforms unsupervised VO, the performance gap between them is diminishing as the limitations of unsupervised VO are gradually addressed. For example, it has been found that unsupervised VO now can recover global scale from monocular images [1]}. Overall, data-driven visual odometry shows a remarkable increase in model performance, indicating the potentials of deep learning approaches in achieving more accurate odometry estimation in the future.
